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arXiv AI By Utsav Poudel, Jagannath Aryal, Subramaniyaswamy Vairavasundaram

Neuro-Geospatial Modelling of EEG Affective States Using Literature-Informed Environmental Context

Read the original on arXiv AI →

The study explores whether environmental data can enhance EEG-based affective-state classification by treating such data as literature-informed priors. Using a dual‑tower model that fuses EEG-Conformer representations with a graph‑based environmental encoder, the authors achieve 76.2% accuracy versus 67.4% for EEG alone on a dataset from Astana. Experiments with controls, dose‑response reversal, and domain‑shift show that the improvement is not solely due to environmental information, and replacing Astana’s environmental distribution with Singapore’s reduces accuracy to 72.8%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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